Kinematics-aware model predictive control for autonomous high-speed tracked vehicles under the off-road conditions

Although accurate trajectory tracking of autonomous vehicles strongly depends on a precise model, it is difficult to establish a precise model for high-speed tracked vehicles under off-road conditions, due to the complicated interactions between the tracks and the terrain. This paper presents a novel trajectory tracking methodology, called kinematics-aware model predictive control (KAMPC), by combining the slip kinematic model with a trajectory tracking control strategy for skid-steered tracked vehicles. For the slip kinematic model, an online identification methodology called six-parameter slip parameter estimation (SSPE) algorithm is proposed based on the instantaneous centers of rotation. For the trajectory tracking control strategy, optimized algorithm model predictive control is used to obtain the optimal control inputs. Finally, our method is validated through extensive experiments based on a distributed electric-drive high-speed tracked vehicle test platform over different types of terrain and varied off-road surface conditions. Experiment results show that the tracked vehicle kinematic characteristics are distinctive in off-road conditions, and the slipping and skidding that occur when turning are critical to this work. Compared with the other three widely used types of methodologies, the present KAMPC approach can reduce the average tracking residuals and has the best statistical performance.